An improved extended state estimation approach for maneuvering target tracking using random matrix

Abstract

The Gaussian inverse Wishart (GIW) filter is a promising filter for extended target tracking and draws tremendous attention in recent years. The Gaussian and the inverse Wishart distributions are used to describe the target's kinematical and extended states, respectively. However, the filter for estimating the extended state contains predicting position error and causes large error of the extended state estimation, especially for the scenarios with high-maneuvering. In this paper, we eliminate the influence of the predicting position error via reconstructing the updated equation for estimating extended state. Based on GIW probability hypotheses density (GIW-PHD) framework, the improved filter is tested in a maneuvering scenario and the comparative results verify the superior performance of the filter in terms of the extended state estimation.